The Cognition/Metacognition Trade-Off

David Rosenbaum*, Moshe Glickman, Stephen M. Fleming, Marius Usher

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Integration to boundary is an optimal decision algorithm that accumulates evidence until the posterior reaches a decision boundary, resulting in the fastest decisions for a target accuracy. Here, we demonstrated that this advantage incurs a cost in metacognitive accuracy (confidence), generating a cognition/metacognition trade-off. Using computational modeling, we found that integration to a fixed boundary results in less variability in evidence integration and thus reduces metacognitive accuracy, compared with a collapsing-boundary or a random-timer strategy. We examined how decision strategy affects metacognitive accuracy in three cross-domain experiments, in which 102 university students completed a free-response session (evidence terminated by the participant’s response) and an interrogation session (fixed number of evidence samples controlled by the experimenter). In both sessions, participants observed a sequence of evidence and reported their choice and confidence. As predicted, the interrogation protocol (preventing integration to boundary) enhanced metacognitive accuracy. We also found that in the free-response sessions, participants integrated evidence to a collapsing boundary—a strategy that achieves an efficient compromise between optimizing choice and metacognitive accuracy.

Original languageEnglish
Pages (from-to)613-628
Number of pages16
JournalPsychological Science
Issue number4
StatePublished - Apr 2022


  • computational models
  • decision confidence
  • decision-making
  • diffusion model
  • integration to boundary
  • judgment
  • metacognition
  • optimality
  • preregistered
  • reaction time


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